bio-clip-seq-stamp-antibody-free
Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.
npx skills add BioTender-max/awesome-bio-agent-skills --skill stamp-antibody-free --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: STAMP / scSTAMP (Brannan 2021/2024 Yeo lab github), Bullseye 1.0+, SAILOR 1.1+, samtools 1.19+, REDItools2 1.3+, JACUSA2 2.0+, scanpy 1.10+, anndata 0.10+, pysam 0.22+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws unexpected errors, introspect the installed package and adapt the example to match the actual CLI rather than retrying. # STAMP / Antibody-Free RBP Profiling **"Profile RBP-RNA targets without UV crosslinking or immunoprecipitation"** -> Express a fusion of the RBP-of-interest with a deaminase (APOBEC1 for STAMP, ADAR for TRIBE) in cells; the deaminase edits RNA nucleotides adjacent to where the RBP binds, producing a C-to-U (STAMP) or A-to-I (TRIBE, read as A-to-G) editing signature in standard RNA-seq. The targets are recovered computationally from the editing pattern. Three properties make this approach valuable: (a) no UV crosslinking required (works in tissue/in vivo); (b) no IP step (no antibody needed - the RBP itself
- Version Compatibility
- Methods Taxonomy
- STAMP vs m6A-Specific Methods
- Critical Choice: STAMP (APOBEC1) vs TRIBE (ADAR)
- scSTAMP / scTRIBE Single-Cell Workflow
- Editing-Site Detection Pipelines
- Per-Method Failure Modes
- STAMP -- APOBEC1 over-expression saturation
- STAMP -- APOBEC1 off-target on ssDNA
- TRIBE -- Editing at ALU repeats
- DART-seq -- Spatial offset from m6A
- scSTAMP -- Coverage limitation per cell
- No control subtraction
- Strand-specific edit interpretation
Standard 10x cellranger pipeline produces BAM with per-cell barcodes cellranger count \ scSTAMP analysis (Yeo lab github) Quantify per-cell C->U editing python scstamp_analysis.py \ Bullseye -- conceptual STAMP workflow (multi-step Perl scripts; verify against repo) perl parseBAM.pl --input stamp_sample.bam --output stamp.parsed.tsv perl parseBAM.pl --input apobec1_only.bam --output control.parsed.tsv perl summarize_sites.pl --in stamp.parsed.tsv > stamp.summary.tsv perl summarize_sites.pl --in control.parsed.tsv > control.summary.tsv
What does the bio-clip-seq-stamp-antibody-free skill do?
Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill stamp-antibody-free --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.